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All Journal JOIV : International Journal on Informatics Visualization Journal of Emerging Science and Engineering International Journal of Renewable Energy Development
Paramasivam, Prabhu
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science

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Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms Le, Thanh Tuan; Paramasivam, Prabhu; Adril, Elvis; Nguyen, Van Quy; Le, Minh Xuan; Duong, Minh Thai; Le, Huu Cuong; Nguyen, Anh Quan
International Journal of Renewable Energy Development Vol 13, No 4 (2024): July 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60387

Abstract

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects
Nanotechnology-based biodiesel: A comprehensive review on production, and utilization in diesel engine as a substitute of diesel fuel Le, Thanh Tuan; Tran, Minh Ho; Nguyen, Quang Chien; Le, Huu Cuong; Nguyen, Van Quy; Cao, Dao Nam; Paramasivam, Prabhu
International Journal of Renewable Energy Development Vol 13, No 3 (2024): May 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60126

Abstract

As a sustainable replacement for fossil fuels, biodiesel is a game-changer in the energy sector. There is no strategy to minimize biodiesel's significance as a sustainable, clean fuel source in light of the increasing climate change and environmental sustainability concerns. Nevertheless, conventional biodiesel production methods often run into problems like inadequate conversion efficiency and inappropriate fuel properties, which impede their broad adoption. The revolutionary potential of nanotechnology to circumvent these limitations and revolutionize biodiesel consumption and production is explored in this review paper. There are new possibilities for improving biodiesel output and engine efficiency, thanks to nanotechnology, which can alter matter at the atomic and molecular levels. Using nano-catalysts, nano-emulsification processes, and nano-encapsulation procedures, researchers have made significant advances in improving biodiesel qualities such as stability, combustion efficiency, and viscosity. Through a comprehensive analysis of current literature and research data, this article elucidates the crucial role of nanotechnology in advancing biodiesel technology. By shedding light on the most current advancements, challenges, and potential future outcomes in nano-based biodiesel manufacturing and consumption, this review hopes to add to the growing corpus of knowledge in the field and inspire additional innovation. In conclusion, there is great hope for a sustainable energy future, increased economic growth, and reduced environmental impacts through the application of nanotechnology.  
Soft computing-based modelling and optimization of NOx emission from a variable compression ratio diesel engine Paramasivam, Prabhu; Naima, Khatir; Dzida, Marek
Journal of Emerging Science and Engineering Vol. 2 No. 2 (2024)
Publisher : BIORE Scientia Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/jese.2024.e21

Abstract

Machine learning method and statistical method used for model prediction and optimization of third generation biodiesel-diesel blend powered variable compression engine High R2 values of 0.9998 and 0.9994 were observed in the training and testing phase of the model, respectively, indicating that The results confirm the robustness of the forecasting system. It was shown that the model accuracy means squared errors remained low at 0.0002 and 0.0014. These results were then confirmed by desirability-based optimization, which succeeded in achieving the values of the set parameters It should be noted that the compression ratio (CR), fuel injection pressure, and engine load were optimized to meet the defined parameters, resulting in a NOx emissions reduction as 222.8 ppm. The research illustrates the efficacy of desirability-based optimization in attaining targeted performance targets across important engine parameters whilst also reducing the impact on the environment.
Application of response surface methodology to optimize the dual-fuel engine running on producer gas Nguyen, Phuoc Quy Phong; Tran, Viet Dung; Nguyen, Du; Luong, Cong Nho; Paramasivam, Prabhu
International Journal of Renewable Energy Development Vol 14, No 2 (2025): March 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60927

Abstract

This work develops a computational framework that optimizes the performance and emissions of a dual-fuel diesel engine running on biomass-derived producer gas as the main fuel and diesel as the pilot fuel. The study connects essential responses, brake thermal efficiency, peak combustion pressure, and emissions of nitrogen oxides (NOx), carbon monoxide (CO), and unburnt hydrocarbon (HC) with controllable factors like engine load and pilot fuel injection duration. The approach consists of simulating the impacts of these controllable inputs on engine performance, then optimization to find the optimal fuel injection pressure to balance performance and emissions. The results show that engine load considerably affects NOx emissions and brake thermal efficiency; greater loads lower CO emissions but raise HC emissions at low compression ratios. Although it had little effect on NOx emissions, fuel injection pressure was vital in balancing general engine performance. Using optimization, an optimal fuel injection pressure value of 218.5 bar was identified, thereby producing a brake thermal efficiency of 27.35% and lowering emissions to 80 ppm HC, 202 ppm NOx, and 92 ppm CO. This computational method offers a strategic means for improving the efficiency of dual-fuel engines while reducing their environmental impact, hence guiding more sustainable and effective engine operation.
An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation Vu, Van Vien; Le, Phuoc Tai; Do, Thi Mai Thom; Nguyen, Thi Thuy Hieu; Tran, Nguyen Bao Minh; Paramasivam, Prabhu; Le, Thi Thai; Le, Huu Cuong; Chau, Thanh Hieu
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2641

Abstract

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.
Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy Nguyen, Tien Han; Paramasivam, Prabhu; Dong, Van Huong; Le, Huu Cuong; Nguyen, Duc Chuan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2637

Abstract

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.
Artificial intelligence applications in solar energy Le, Thanh Tuan; Le, Thi Thai; Le, Huu Cuong; Dong, Van Huong; Paramasivam, Prabhu; Chung, Nghia
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2686

Abstract

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.
Co-Authors Cao, Dao Nam Chau, Thanh Hieu Chung, Nghia Do, Thi Mai Thom Dong, Van Huong Duong, Minh Thai Dzida, Marek Elvis Adril Le, Huu Cuong Le, Minh Xuan Le, Phuoc Tai Le, Thanh Tuan Le, Thi Thai Luong, Cong Nho Naima, Khatir Nguyen, Anh Quan Nguyen, Du Nguyen, Duc Chuan Nguyen, Phuoc Quy Phong Nguyen, Quang Chien Nguyen, Thi Thuy Hieu Nguyen, Tien Han Nguyen, Van Quy Tran, Minh Ho Tran, Nguyen Bao Minh Tran, Viet Dung Vu, Van Vien